Аннотация:Filtering is the most common approach to raster processing. In cartography and geographical information science it is used for numerical generalization of surfaces, calculation of derivatives, edge detection and variety of other tasks concerned with image processing. Rasters covering large areas contain all the distortions that are introduced by the projection in which the raster is stored. Examples are global digital elevation models, climatological and land cover rasters, and low resolution space imagery. Processing of such rasters with traditional “fixed-kernel” filtering approach leads to the loss of geographical meaning and incorrect calculation of derivatives, as the same floating window covers different geographical neighbourhoods in different parts of raster. Distortions in raster analysis received moderate attention so far. Steinwand et al (1995) revealed the effects of map projection on data quality. Seong and Usery (2001) developed a scale factor model that is based on calculation of projection scale factors. They assessed scale factors for cylindrical equal area, sinusoidal and Mollweide projections. Usery at al (2003) investigated map projection and resampling effects on the tabulation of categorical areas in global raster datasets. Mulcahy (2000) analysed pixel loss and replication patterns in eight projection of world maps. These investigations are mainly concerned with global effects of distortions. We present new approach to raster processing that applies affine transformation to filtering kernel that compensates for local projection distortions. The following algorithm is proposed: 1.Define initial shape of the kernel (commonly rectangular or ellipse) and its sizes in both X and Y directions 2.Calculate the extent of the raster in geographical units (degrees). 3.Sample raster area by the control points, which are equally spaced in degrees. Sampling distance is defined by user or can be calculated as a function of raster geographical extent and resolution. 4.Calculate the parameters of distortion ellipse at each control point using projection equations. 5.Using distortion ellipse parameters, define the local matrix of affine transformation. 6.Transform initial kernel shape and rasterize it. 7.For each pixel in the initial raster find the closest control point and assign its number to the pixel. 8.Process the whole raster using kernels from the assigned control points. The algorithm was applied to several elevation datasets covering continental Europe and Asia and projected to various conic, cylindrical, pseudocylindrical and azimuthal projections. Results show that variable kernel shape approach can be used for generalization and search of terrain features of the same size. Another important application of the method is the correct calculation of derivatives, for which the anisotropy of the distorted space is important (slope and aspect). We also applied scale factor model to make corrections to the global raster statistics calculated from an arbitrary projection. Presented “geographical” approach to raster processing is opposed to traditional “geometric”. Differentiation of tasks and application fields of both approaches is defined as well as their limitations and perspective directions of development. References: Mulcahy KA (2000) Two new metrics for evaluating pixel-based change in data sets of global extent due to projection transformation. Cartographica, 37:1-11. Steinwand DR, Hutchinson JA, and Snyder JP (1995) Map projections for global and continental data sets and an analysis of pixel distortion caused by reprojection. Photogrammetric Engineering and Remote Sensing, v. 61, no. 12, December, pp. 1487–1497. Seong JC, Usery L (2001) Modeling Raster Representation Accuracy Using a Scale Factor Model. Photogrammetric Engineering Remote Sensing 67:1185–1191. Usery EL, Finn MP, Cox JD, Beard T (2003) Projecting global datasets to achieve equal areas. Cartography and Geographic Information Science 30:69-79